1. Field of the Invention
The present invention relates to the use of customer information in the electronic settlement by credit cards and the like.
2. Description of Related Art
Electronic (paperless) settlements such as credit cards and ATM cards are commonly used. Such settlement requires the use of credit cards held by customers, or recipients of the service, and is carried out in the following manner:
First, a store clerk working at a member store inputs the code and price of merchandises purchased by customers into a card-processing device often located next to a cash register. Next, he slides the credit card through the device and inquires banks or credit card companies through their processing systems connected to the card-processing device to determine if such purchase can be authorized. Each credit card has a magnetic area or an IC memory device, which maintains the customer information such as a control number used by a credit card company and a customer name, or a bank account number.
Credit card companies or banks have processing systems that determine if the customer will be able to pay for the purchase. Based on this determination, if the systems authorize, the customer can receive the goods with his signature or the like. Also, a server of the processing systems maintains specific data necessary to withdraw fund from the customer's bank account.
When such electronic settlements are performed, member stores may want to research consumer purchasing trends through credit card companies since those credit card companies store information such as the purchaser's names, and the purchased items, places and amounts.
For instance, by analyzing data stored in the database, member stores can obtain information such as what merchandise was purchased, where it was purchased, and who made the purchase through observing the merchandise type by customer age group, the total purchase number per location, and the total purchase amount.
To achieve this, the following “data-warehouse building” method can be used:
1. After each business day, one can access to the customer information database, calculate the number of purchases and the purchase amount sorted by a “viewpoint” of analyses (i.e., merchandise, place and date of purchase, purchaser), and store the information in the database.
2. Furthermore, using the information stored in the database, one can perform more focused analyses (i.e., by manufacturer, purchased area, purchased month, and age and gender of the purchaser) and generate reports for the results.
Although basic information tables (generally, use details) in the server also enable comparable analyses, the aforementioned data-warehouse analyses are used because renewing basic information tables such as use details will be a significant burden for direct analyses thereof, particularly during business hours, as such renewal is performed often and the population of data used for analyses has a large volume.
Building a data warehouse secures data that are specific to the analyses and independent from the daily operations. However, as mentioned below, in the credit card industry, this method has some difficulties and has not been implemented. Also, as disclosing customer information to member stores could potentially create problems under the current laws, the open data-warehouse service is currently not provided.
To perform customer analyses using a data warehouse, one needs to accumulate data extracted according to the certain predetermined “viewpoints” and builds a database (data warehouse) sorted by each “viewpoint.” Accordingly, when card service member stores and other operators perform purchase analyses using a database of credit card companies, credit card companies must search their database by each requested “viewpoint.”
However, relatively small database aside, performing analyses using a large database typically owned by credit card companies has presented a challenge as it requires a significant burden to extract necessary information out of all data stored and sorted by each “viewpoint” in the database and to build a data warehouse in conjunction with daily operations.